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Luning Wang

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4 papers

2 author rows

ICLR Conference 2025 Conference Paper

Dynamic Modeling of Patients, Modalities and Tasks via Multi-modal Multi-task Mixture of Experts

Chenwei Wu 0006
Zitao Shuai
Zhengxu Tang
Luning Wang
Liyue Shen

Multi-modal multi-task learning holds significant promise in tackling complex diagnostic tasks and many significant medical imaging problems. It fulfills the needs in real-world diagnosis protocol to leverage information from different data sources and simultaneously perform mutually informative tasks. However, medical imaging domains introduce two key challenges: dynamic modality fusion and modality-task dependence. The quality and amount of task-related information from different modalities could vary significantly across patient samples, due to biological and demographic factors. Traditional fusion methods apply fixed combination strategies that fail to capture this dynamic relationship, potentially underutilizing modalities that carry stronger diagnostic signals for specific patients. Additionally, different clinical tasks may require dynamic feature selection and combination from various modalities, a phenomenon we term “modality-task dependence.” To address these issues, we propose M4oE, a novel Multi-modal Multi-task Mixture of Experts framework for precise Medical diagnosis. M4oE comprises Modality-Specific (MSoE) modules and a Modality-shared Modality-Task MoE (MToE) module. With collaboration from both modules, our model dynamically decomposes and learns distinct and shared information from different modalities and achieves dynamic fusion. MToE provides a joint probability model of modalities and tasks by using experts as a link and encourages experts to learn modality-task dependence via conditional mutual information loss. By doing so, M4oE offers sample and population-level interpretability of modality contributions. We evaluate M4oE on four public multi-modal medical benchmark datasets for solving two important medical diagnostic problems including breast cancer screening and retinal disease diagnosis. Results demonstrate our method's superiority over state-of-the-art methods under different metrics of classification and segmentation tasks like Accuracy, AUROC, AUPRC, and DICE.

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UAI Conference 2024 Conference Paper

Consistency Regularization for Domain Generalization with Logit Attribution Matching

Han Gao 0016
Kaican Li
Weiyan Xie
Zhi Lin
Yongxiang Huang
Luning Wang
Caleb Chen Cao
Nevin L. Zhang

Domain generalization (DG) is about training models that generalize well under domain shift. Previous research on DG has been conducted mostly in single-source or multi-source settings. In this paper, we consider a third lesser-known setting where a training domain is endowed with a collection of pairs of examples that share the same semantic information. Such semantic sharing (SS) pairs can be created via data augmentation and then utilized for consistency regularization (CR). We present a theory showing CR is conducive to DG and propose a novel CR method called Logit Attribution Matching (LAM). We conduct experiments on five DG benchmarks and four pretrained models with SS pairs created by both generic and targeted data augmentation methods. LAM outperforms representative single/multi-source DG methods and various CR methods that leverage SS pairs. The code and data of this project are available at https: //github. com/Gaohan123/LAM.

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ICML Conference 2024 Conference Paper

Evaluating Quantized Large Language Models

Shiyao Li
Xuefei Ning
Luning Wang
Tengxuan Liu
Xiangsheng Shi
Shengen Yan
Guohao Dai 0001
Huazhong Yang

Post-training quantization (PTQ) has emerged as a promising technique to reduce the cost of large language models (LLMs). Specifically, PTQ can effectively mitigate memory consumption and reduce computational overhead in LLMs. To meet the requirements of both high efficiency and performance across diverse scenarios, a comprehensive evaluation of quantized LLMs is essential to guide the selection of quantization methods. This paper presents a thorough evaluation of these factors by evaluating the effect of PTQ on Weight, Activation, and KV Cache on 11 model families, including OPT, LLaMA2, Falcon, Bloomz, Mistral, ChatGLM, Vicuna, LongChat, StableLM, Gemma, and Mamba, with parameters ranging from 125M to 180B. The evaluation encompasses five types of tasks: basic NLP, emergent ability, trustworthiness, dialogue, and long-context tasks. Moreover, we also evaluate the state-of-the-art (SOTA) quantization methods to demonstrate their applicability. Based on the extensive experiments, we systematically summarize the effect of quantization, provide recommendations to apply quantization techniques, and point out future directions. The code can be found in https: //github. com/thu-nics/qllm-eval.

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YNIMG Journal 2012 Journal Article

Altered spontaneous activity in Alzheimer's disease and mild cognitive impairment revealed by Regional Homogeneity

Zengqiang Zhang
Yong Liu
Tianzi Jiang
Bo Zhou
Ningyu An
Haitao Dai
Pan Wang
Yixuan Niu

Alzheimer's disease (AD), the most prevalent cause of dementia in the elderly, is characterized by progressive cognitive and intellectual deficits. Most patients with mild cognitive impairment (MCI) are thought to be in a very early stage of AD. Resting-state functional magnetic resonance imaging reflects spontaneous brain activities and/or the endogenous/background neurophysiological process of the human brain. Regional Homogeneity (ReHo) can provide a fast method for mapping regional activity across the whole brain. Little has been previously published about where or how spontaneous activity differs between MCI and AD, although many previous fMRI studies have shown that the activity pattern is altered in MCI/AD. In the present study, we first used the ReHo method to explore differences in regional spontaneous activities throughout the whole brain between normal controls (NC) and people with MCI and with AD. A one-way ANOVA was performed to determine the regions in which the ReHo differs between the three groups, and then a post hoc analysis was performed to evaluate differences in the pattern among the three groups. Finally a correlation analysis was done between the ReHo index of these regions and clinical variables in order to evaluate the relationship between ReHo and cognitive measures in the AD and MCI groups. An exploratory classification analysis also demonstrated that ReHo measures were able to correctly separate subjects in 71. 4% of the cases. Altered brain spontaneous activations were found in the medial prefrontal cortex, the bilateral posterior cingulate gyrus/precuneus and the left inferior parietal lobule (IPL) in both MCI and AD. In MCI, the ReHo index in the left IPL was higher than that of the NC, which could indicate the presence of a compensatory mechanism in MCI. More obviously, the correlation analysis indicated that the lower the memory and other cognitive abilities, the lower the ReHo in patients with MCI and AD. Combining our findings with the results in earlier studies, we propose that the spontaneous activity pattern in the resting state could potentially be used as a clinical marker for MCI/AD.

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